The Emerging Technologies of Point Cloud: How it can benefit your business

This guide outlines how point cloud technology can help your business create accurate models and improve workflow with a special focus on construction and healthcare.

Poonkuzhale K

The Emerging Technologies of Point Cloud: How it can benefit your business

Improved workflow and accuracy achieved!

Every year brings new advancements in software and hardware, making it more critical than ever for end users to be acquainted with state of the art in point cloud technology and how it can improve business operations.

Since its introduction, point cloud technology has revolutionized many fields and processes. Point cloud services have surpassed their initial role as a substitute for 3D-CAD modeling and are now embedded in nearly every aspect of the software industry, from development to operations to user experience design. Numerous industries, including building and construction, retail, manufacturing, gaming, and healthcare, could benefit significantly from this technology.

Point-Cloud Technology: What Is It?

Rising computing power and advancements in underlying technology are driving the rising demand for Point Cloud Technology. Point Cloud Technology can create a 3D representation of any object or space using laser-placed "points" on visible surfaces instead of traditional primitive shapes or manual data collection. When the point density and resolution requirements are met, point-cloud models can accurately represent the characteristics of virtually any three-dimensional object or space. Given its speed and precision, Point Cloud Modeling is the method of choice for any 3D scanning endeavor.

Your company can benefit from point cloud technology by making more precise models and increasing productivity. You can modify the digital model without having to handle the real thing. Modeling a car, for instance, would only be possible by using a point cloud because of the object's size and location. Point cloud technology can also be used for other purposes, such as keeping tabs on vital equipment in out-of-the-way places or monitoring the movement of goods all along the supply chain.

Blender is one example of free software that can experiment with point cloud technology; SolidWorks and AutoCAD are commercial applications that require a license fee; and cloud-based version services, such as Google Cloud Platform and Amazon Web Services.

Now that we have a basic understanding of the technology, let's delve more deeply into it and perform feature extraction to investigate its tendencies.

Trends of Point Cloud

The ability to separate small subsets of elements from the large-point cloud lets things be categorized, counted, and assigned. Here are some trends driving this.

Point Cloud Data showing a 3D view (Image source:


Point clouds are XYZ data points. It is a large but sparse three-dimensional representation of a scene. By extracting and classifying objects, you can interpret and analyze their output more easily by color-coding point clouds, applying data tags, or both.

As important as it is to the reality capture process, classification must be automated because human selection takes too much time. As the number of use cases and the size of the point cloud increase, its applicability diminishes. Fortunately, this issue can be fixed by implementing an automated classification system for LiDAR data. Robust algorithms are becoming increasingly feasible, facilitating the automatic application of classification to point cloud data.


Light detection and ranging (Lidar) is a type of optical remote sensing that uses laser light to densely sample Earth's surface, resulting in exact x, y, and z coordinates. Compared to more conventional surveying methods like photogrammetry, using Lidar in airborne laser mapping applications offers considerable cost savings.


Photogrammetry uses photographs, measurements, and analyses of electromagnetic radiant imagery and other phenomena to reliably gather data about the physical world and the places in it.

Cloud-based processing

Processing of point clouds is shifting to the cloud. Cloud computing is ideal for any point cloud processing application due to its ample processing power, scalability, robust infrastructure, and nearly infinite storage capacity. There will be a rise in the number of cloud-native applications that can be scaled up or down based on demand. This parallelization capability of point cloud processing software has already enabled the scalability of resources to accelerate the processing of massive point cloud data sets significantly.

Performance enhancements continually updated by a faster cloud infrastructure can result in a rise in speed-which is primarily because of

  1. Task parallelization enables speedier processing.
  2. Quick registration and processing, direct file transfer to the cloud from the field.

A phenomenon called "data gravity" describes how data tends to gravitate toward locations that already contain the most information. In a nutshell, it is simpler to move applications to the data rather than the other way around; an illustration of this would be the utilization of a cloud-based "Common Data Environment" with BIM (Building Information Modeling).

Data Gravity?

The term "data gravity" refers to the capacity of a data set to draw in related resources like programs and services.

One way to conceptualize the gravitational pull of data is in terms of the software, services, and business logic surrounding it (the amount of data). The greater the quantity of data, the greater the number of applications, services, and other data types that can be drawn.

Hybrid data sets

Integrating various scan outputs will inevitably become crucial to the future of reality capture. Combining different types of scanning, each with its advantages and disadvantages, can help you get the most out of the available data capture resources. Terrain-based laser scanners, for instance, are well-suited for indoor surveying spaces, factories, and public works projects. Reconstructing 3D shapes and appearances from photos taken from an unmanned aerial vehicle (UAV) makes it possible to take pictures of buildings and their surroundings quickly. Automatic, low-precision, and speedy data collection for both indoor and outdoor environments is being made possible by SLAM (Simultaneous Localization and Mapping). Consolidating and applying these various forms of data to point cloud classification processes will become essential soon.

Point cloud registration

Various point cloud registration methods are used in medical image fusion or registration. A prior cloud registration algorithm was used to register nervous system point clouds. This method solves brain deformation in neurosurgery and achieves millimeter-accurate point cloud registration preoperatively and intraoperatively. Later, point cloud registration and ultrasound-based surgical navigation were used to create a non-invasive, non-radiative image navigation system.

With the new Adaptive Super4PCS Algorithm, neurosurgery image navigation is more optimized. The process begins with the creation of the point cloud of the medical image. Intraoperatively, a Kinect II device scans the patient's exposed head, and a partial point cloud model B is generated for preoperative and intraoperative registration. Different intracranial anatomical structures are then generated using preoperative MRI and CT images and other threshold windows, and finally, the entire point cloud model A to be registered is developed.

In the image below, you can see 

a) The standard medical spine model

(b) is a CT-scanned point cloud of a medical spine model.

(c) is a point cloud created from a single frontal scan with the Kinect II.

(d) is the result of point cloud registration. There is a positive cloud registration effect for the two-point clouds.

3D Registration of Point Cloud Data Using Parameter Adaptive Super4PCS Algorithm in Medical Image Analysis (Image source:

Here's How Your Company Can Benefit from Point-Cloud Technology

  • Encapsulation is an emerging point-cloud technology that protects sensitive information from potential threats.
  • By using 3D models, you can create a more precise representation of our physical environment by capturing point clouds and using them to create 3D models.
  • To model something, you can automatically collect data about each point and then use that data to produce other data about the things in those locations.
  • As it relates to medicine, point clouds can help the aging-in-place movement gain traction. Based on a BIM model, it can be used to assess whether or not a given space is suitable for people who require special care, including those with mobility impairments.
  • LiDAR's point clouds are exact and accurate, outlining features of Earth's surface in great detail. This makes point clouds an excellent resource for precise mapping because they can be used to spot and emphasize anomalies easily.
  • Its application in medicine comes from 3D Registration and 3D Transformers of the point cloud data using various algorithms in medical image analysis which helps doctors to have accurate images of the neuro-system that which also helps them during surgeries and treatments.
  • Used for city building and remodeling.
  • An innovative technique for collecting 3D information about buildings, and landscapes, for research, renovation, and maintenance.

These are just a few use cases quoted, but as technology evolves, point cloud services increasingly prove their perfection in various industrial spheres and approach an unsurpassed level of quality and precision in solving tasks of all complexity.

Automatic point cloud classification for construction. (Image source:

Positive Aspects of Point Cloud Technology

Enhanced Project Management

Point Cloud Technology has significantly boosted the effectiveness of building plans. The field of project management is one in which Point Cloud Technology excels. Architects, engineers, and construction crews have a reliable method for creating highly accurate 3D models, or "digital twins", of real-world locations through scanning, building, and tweaking. Point Clouds offer a complex system that streamlines the construction of those models based on the actual size and real-world dimensions, while Building Information Modeling is gradually replacing traditional CAD data. In addition, these scans can be used throughout a construction project at any stage to guarantee precise documentation and check that everything is being done according to plan.

Greater accuracy

Faster and more accurate results are achieved when capturing a landscape or area as the basis for a 3D model. Drone-based LIDAR is accurate to within 1-30 centimeters, while ground-based LIDAR can provide accurate results within a millimeter.

Accuracy is a potential asset in the field of medicine, particularly regarding the performance of surgical procedures. Super4PCS, the most recent algorithm for medical image registration, allows for sub-millimeter accuracy. Aside from the core LIDAR functionality, additional features like GPS are often incorporated to guarantee each data point's accuracy for landscape documentation.

Value for Money

If you do your best to stick within your budget, you'll have a lower chance of incurring shockingly serious mistakes or unanticipated expenses. Site planning with point cloud allows for greater precision, creating a more compelling budget. Laser inspection also eliminates the need for manual review, which helps keep labour costs down. When first developed, Point Cloud Technology was only available to the largest AEC (Architectural Engineering and Construction) firms. However, as the price of this technology has decreased, it is now within reach of smaller firms, allowing them to take advantage of its ability to streamline their processes and save time and money on their projects.

Engineering in iterations

The BIM models' ability to facilitate iterative design processes is a significant advantage. Prototyping, testing, analyzing, and refining a design or system in an iterative fashion is the foundation of the iterative design approach, which may be unfamiliar to some.

With the help of Point Cloud Technology, it is now possible to use a scan of a project to generate the initial parameters and constraints or to evaluate the project's success by contrasting the final product with the original design's goals. 

Post-project perfection

Helpful for documenting what's behind the building's walls, ceilings, and other hidden areas, Point Cloud Technology helps by updating BIM models in real-time throughout construction. This is a significant benefit for any building manager, but it is vital for those in charge of complex or older buildings (or both). Now more than ever, thanks to advancements in Point Cloud Technology, all facility maintenance records, visible and invisible alike, can be stored in one easily accessible location.

Improved Safety

Manual surveying often necessitates navigating treacherous terrain, which risks the surveyors' safety. Point clouds use drones to collect data safely from these hazardous environments, avoiding the risk of accidents and injuries.

You had no idea that point cloud could provide many benefits, right? In today's era of digitization and technological progress, Point Cloud Modeling has become an indispensable tool for providing surveyors, surgeons and engineers with quick, accurate, and simple answers.


A business's productivity can be seriously hampered by inefficient manual data collection processes that cause significant delays. Any technology that has the potential to improve upon this will likely catch the eye of business and industry leaders who are constantly looking to speed up the efficiency of organizational tasks. The point cloud is one such technology, and it's still relatively new—that being said, it's already proven itself as an invaluable tool that you'll want to integrate into your workflow sooner rather than later if you haven't done so. If you're at all interested in 3D imaging or if it has applications for your business, then this guide should be a good resource for you.

What can Performix do?

Our machine-learning-based solution for analyzing 3D point clouds constitutes a big data analytics-based analysis of this type of data. Manufacturing, design, surveying, asset management, transportation infrastructure, real estate management, insurance, medical image registration, and the public sector are just some industries that use our platform.

So, the next time you need to model suitable old-fashioned objects, give point clouds a try. You'll never know where it might take you!

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